ABSTRACT
Recently, COVID-19 is spreading rapidly and fast detection of COVID-19 can save millions of lives. Further, the COVID-19 can be detected easily from chest x ray (CXR) images using artificial intelligence methods. However, the performance of these application and methods are reduced due to noises presented in the CXR images, which degrading the performance of overall systems. Therefore, this article is focused on implementation of an innovative method for quickly processing CXR images of low quality, which enhances the contrast using fuzzy logic. This method makes use of tuned fuzzy intensification operators and is intended to speed up the processing time. Therefore, this work is focused on implementation of CXR image-based disease classification network (CIDC-Net) for identification of COVID-19 and pneumonia related 21 diseases. The CIDC-Net utilizes the deep learning convolutional neural network (CNN) model for training and testing. Finally, the simulations revealed that the proposed CIDC-Net resulted in superior performance as compared to existing models. © 2022 IEEE.